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The use of deep learning to forecast Alzheimer’s disease is a highly interesting field of medical research. Research has shown that deep learning models—in particular, convolutional neural networks, or CNNs—are very good at recognizing Alzheimer’s from magnetic resonance imaging (MRI). Studies show that CNNs are able to identify intricate details from MRI pictures and can be trained to differentiate among those who have Alzheimer’s disease and those who do not. The models are trained on large MRI scan datasets and then utilized in a variety of ways, including data augmentation and transfer learning, to increase their accuracy. The application of deep learning models to forecast Alzheimer’s disease could significantly improve early diagnosis and treatment of the condition while also fostering the development of new treatments.

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A Unique and Effective Deep Learning Approach for Alzheimer’s Disease Prediction

  • 会议论文

  • 发表日期：

    2025年

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  • Sireesha Moturi,
  • M. Mounika Naga Bhavani,
  • K. B. Anusha,
  • Modalavalasa Divya

摘要

The use of deep learning to forecast Alzheimer’s disease is a highly interesting field of medical research. Research has shown that deep learning models—in particular, convolutional neural networks, or CNNs—are very good at recognizing Alzheimer’s from magnetic resonance imaging (MRI). Studies show that CNNs are able to identify intricate details from MRI pictures and can be trained to differentiate among those who have Alzheimer’s disease and those who do not. The models are trained on large MRI scan datasets and then utilized in a variety of ways, including data augmentation and transfer learning, to increase their accuracy. The application of deep learning models to forecast Alzheimer’s disease could significantly improve early diagnosis and treatment of the condition while also fostering the development of new treatments.